Ruslan Salakhutdinov

Canada Research Chair in Statistical Machine Learning

Assistant Professor
Microsoft Faculty Fellow
Sloan Fellow
University of Toronto
rsalakhu[at]cs.toronto.edu
CV Google Scholar  

In the Spring of 2016, I will be moving to the Machine Learning Department at Carnegie Mellon University. I am looking for strong PhD students, please apply to CMU if you are interested in working with me.

I am an assistant professor of Computer Science and Statistics at the University of Toronto. I work in the field of statistical machine learning (See my CV.) I received my PhD in computer science from the University of Toronto in 2009. After spending two post-doctoral years at MIT, I joined the University of Toronto in 2011.

My research interests include Deep Learning, Probabilistic Graphical Models, and Large-scale Optimization.

Prospective students: Please read this to ensure that I read your email.

Recent Research Highlights:




Recent Papers:

  • Importance Weighted Autoencoders
    Yuri Burda, Roger Grosse, Ruslan Salakhutdinov
    ICLR, 2016, [arXiv]. Code is available [here].

  • Actor-Mimic: Deep Multitask and Transfer Reinforcement Learning
    Emilio Parisotto, Jimmy Lei Ba, Ruslan Salakhutdinov
    ICLR, 2016, [arXiv].

  • Generating Images from Captions with Attention
    Elman Mansimov, Emilio Parisotto, Jimmy Lei Ba, Ruslan Salakhutdinov
    ICLR, 2016, oral [arXiv]. [Generated Samples].


  • Data-Dependent Path Normalization in Neural Networks
    Behnam Neyshabur, Ryota Tomioka, Ruslan Salakhutdinov, Nathan Srebro
    ICLR, 2016, [arXiv].

  • Deep Kernel Learning
    Andrew Gordon Wilson, Zhiting Hu, Ruslan Salakhutdinov, Eric Xing
    AI and Statistics, 2016, [arXiv].

  • Human-level concept learning through probabilistic program induction
    Brenden Lake, Ruslan Salakhutdinov, and Joshua Tenenbaum (2015),
    Science, 350(6266), 1332-1338, [paper], [Supporting Info.], [visual Turing tests], [Omniglot data set], [Code].


  • Action Recognition using Visual Attention
    Shikhar Sharma, Ryan Kiros, Ruslan Salakhutdinov
    [arXiv]. [Code]. [Project Website].

  • Learning Wake-Sleep Recurrent Attention Models
    Lei Jimmy Ba, Roger Grosse, Ruslan Salakhutdinov, Brendan Frey
    NIPS 2015. [arXiv].

  • Skip-Thought Vectors
    Ryan Kiros, Yukun Zhu, Ruslan Salakhutdinov, Richard S. Zemel, Antonio Torralba, Raquel Urtasun, Sanja Fidler
    NIPS 2015, [arXiv].

  • Path-SGD: Path-Normalized Optimization in Deep Neural Networks
    Behnam Neyshabur, Ruslan Salakhutdinov, Nathan Srebro
    NIPS 2015, [arXiv].

  • Aligning Books and Movies: Towards Story-like Visual Explanations by Watching Movies and Reading Books
    Yukun Zhu, Ryan Kiros, Richard Zemel, Ruslan Salakhutdinov, Raquel Urtasun, Antonio Torralba, Sanja Fidler
    ICCV 2015, [arXiv], oral , [ project page ]

  • Predicting Deep Zero-Shot Convolutional Neural Networks using Textual Descriptions
    Jimmy Ba, Kevin Swersky, Sanja Fidler, Ruslan Salakhutdinov
    ICCV 2015, [arXiv].

  • Learning Deep Generative Models
    Ruslan Salakhutdinov
    Annual Review of Statistics and Its Application, Vol. 2, pp. 361–385, 2015
    [pdf].

  • Scaling Up Natural Gradient by Sparsely Factorizing the Inverse Fisher Matrix
    Roger Grosse, Ruslan Salakhutdinov
    ICML, 2015. [pdf].

  • Unsupervised Learning of Video Representations using LSTMs
    Nitish Srivastava, Elman Mansimov, Ruslan Salakhutdinov
    ICML, 2015, [arXiv], [Code]

  • Show, Attend and Tell: Neural Image Caption Generation with Visual Attention
    Kelvin Xu, Jimmy Ba, Ryan Kiros, Kyunghyun Cho, Aaron Courville, Ruslan Salakhutdinov, Richard Zemel, Yoshua Bengio
    ICML, 2015, [arXiv], [project page],
  • Siamese neural networks for one-shot image recognition.
    Gregory Koch, Richard Zemel, Ruslan Salakhutdinov
    ICML 2015 Deep Learning Workshop (2015), [pdf].

  • Exploiting Image-trained CNN Architectures for Unconstrained Video Classification
    Shengxin Zha, Florian Luisier, Walter Andrews, Nitish Srivastava, Ruslan Salakhutdinov
    BMVC 2015, [arXiv], 2015

  • segDeepM: Exploiting Segmentation and Context in Deep Neural Networks for Object Detection
    Y. Zhu, R. Urtasun, R. Salakhutdinov and S.Fidler
    In Conference on Computer Vision and Pattern Recognition (CVPR), Boston, MA, USA, June 2015,
    [ arXiv ]

  • Unifying Visual-Semantic Embeddings with Multimodal Neural Language Models
    Ryan Kiros, Ruslan Salakhutdinov, Richard Zemel.
    To appear in Transactions of the Association for Computational Linguistics (TACL), 2015.
    [ arXiv], [ results], [ demo ].
    An encoder-decoder architecture for ranking and generating image descriptions.
    Previous version appeared in NIPS Deep Learning Workshop, 2014.

  • Accurate and Conservative Estimates of MRF Log-likelihood using Reverse Annealing
    Yuri Burda, Roger B. Grosse, and Ruslan Salakhutdinov,
    AI and Statistics, 2015 [arXiv]

  • Learning Generative Models with Visual Attention
    Yichuan Tang, Nitish Srivastava, and Ruslan Salakhutdinov
    Neural Information Processing Systems (NIPS 28), 2014, oral,
    [ pdf ], Supplementary material [ pdf].

  • A Multiplicative Model for Learning Distributed Text-Based Attribute Representations
    Ryan Kiros, Richard Zemel, Ruslan Salakhutdinov.
    Neural Information Processing Systems (NIPS 28), 2014.
    [ pdf ], Supplementary material [ zip].

  • Multimodal Learning with Deep Boltzmann Machines
    Nitish Srivastava and Ruslan Salakhutdinov
    Journal of Machine Learning Research, 2014. [ pdf ]. Code is available [ here].